Leveraging Modeling & Simulation in Oncology

Insights from modeling and simulation (M&S) can help to overcome critical challenges associated with oncology clinical trials, by quantitatively integrating knowledge and relationships between the disease, drug characteristics, patient populations, and clinical trial parameters. M&S is used to fill in gaps related to limited data and extend the findings from existing trials for different scenarios and expanded patient populations. Such effort produces deeper understanding of a drug candidate’s efficacy and safety profile and can help to streamline the clinical trial and drug development process, thereby reducing patient burden, risk of failure, and time to market.

A variety of challenges compound oncology drug development. They include:

•  Patients with cancer who are enrolled in clinical trials are often already very sick and typically have comorbidities and numerous comedications.

•  For some targeted therapies, clinical trials must enroll only patients with a specific oncologic profile, such as the presence or absence of a desired genetic mutation or other biomarker.

•  Most oncology drugs are cytotoxic or genotoxic, and thus cannot be studied in healthy volunteers; similarly, targeted agents that can be studied in healthy volunteers may be limited in dose due to potential toxicities.

Meanwhile, to reduce the patient burden, and streamline the drug development and regulatory approval processes, many oncology clinical trials are limited in scope and duration, and single-arm clinical trials are often leveraged to gain initial accelerated or conditional approval from regulators. This approach limits which patient subpopulations and doses are evaluated in the trial setting-leaving sizable gaps in understanding for drug investigators and regulators. While this approach can speed patient access to the new therapy, it also puts added pressure on investigators, and highlights the importance of M&S tools that can produce greater insight from the limited available trial data.

Today, there is increased acceptance of M&S results and growing encouragement from regulatory agencies to use M&S tools. FDA Commissioner Scott Gottlieb recently included “the more widespread use of modeling and simulation, the greater use of real-world evidence in the pre- and post-market setting, and the adoption of better tools for collecting and evaluating more real-time safety information after products are approved” among the new scientific domains that have been introduced into the development and review process. Furthermore, the European Medicines Agency (EMA) just upgraded the reach of M&S within that agency.

Specifically, advanced M&S approaches continue to improve oncology clinical trials by helping investigators to:

•  Plan, inform, analyze, and extend clinical trial data and conduct standalone virtual trials to provide supplemental understanding, taking into account inherent patient-to-patient heterogeneity in terms of their response to therapy and tolerability to treatment.

•  Develop objective response data to show how drug-mediated, tumor-growth inhibition impacts both overall survival and other relevant surrogate endpoints (such as objective response rates, progression-free survival, disease-free survival, and patient-reported outcomes).

•  Quantitatively evaluate and compare the efficacy-safety profile of a new drug against the standard of care or other existing treatment options.

•  Assess go/no-go decisions, support comparator-effectiveness studies, and streamline regulatory filings.

Fine-tuning dose determination

Poor or ill-informed dose selection is often to blame for failed trials, delays, and denials of regulatory submissions, and changes in dosing post-approval. M&S is being increasingly leveraged to improve dose escalation and determination of first-in-human (FIH) doses, to predict and analyze variable dose-response, and to optimize dose-regimen decisions-more broadly and more comprehensively than is possible using the traditional approach of hypothesis testing in a limited trial setting. M&S can be used to both interpolate and extrapolate existing trial data related to specific tested doses, compare with existing treatment options leveraging publicly-available data from competitor compounds, and thus investigate other possible doses and dosing strategies without the need for additional human subjects or dedicated studies.

Historically, oncology drug development involving traditional cytotoxic agents relied on maximizing toxicity, using the maximum tolerated dose (MTD) as the key indicator for maximizing treatment efficacy. Today’s newer biologic agents and immuno-oncology therapies are often able to provide an efficacious dose well before drug levels have become toxic. Thus, clinical trials for such novel oncology agents must work to identify optimal doses and dosing frequency below the MTD to achieve the needed efficacy with better tolerability for the patient. This considerably complex undertaking is greatly enhanced using M&S.

Managing drug-drug interactions

Due to polypharmacy, patients with cancer are at risk of multiple drug-drug interactions (DDIs) and it is impossible for clinical investigators to conduct an endless array of dedicated studies and trials to identify and understand all potential DDIs that may arise for a given oncology agent. Instead, investigators are increasingly turning to quantitative modeling techniques, which can produce rational, data-driven predictions about how the drug’s absorption, distribution, metabolism, and excretion profile will impact different DDI combinations across many different simulated patient cohorts. The resulting insight can be used to inform drug labeling, and also help guide the inclusion or exclusion of patients taking DDI-implicated drugs during clinical trial design and post-marketing studies.

Today, promising work is also underway using modeling techniques to identify potentially advantageous DDIs-that is, specific combinations of investigational and approved drugs that may be able to improve clinical outcomes or tolerability.

Predicting drug activity in virtual patient cohorts

M&S lets investigators leverage relationships elucidated in the existing trial data to predict the drug-exposure impacts, clinical efficacy, and toxicity of investigational therapies in specific modeled patient sub-populations. For instance, such virtual patient cohorts may be structured by common variants such as age, gender, ethnicity, and weight, but also by more complex characteristics such as the presence or absence of specific genetic mutations or other biomarkers, specific comorbidities and coprescribed medications, organ impairment, and vulnerable patient groups (e.g., pediatric or geriatric patients and pregnant women).

Using modeling to assess the competitive landscape

As the number of oncology agents continues to grow, drug developers must demonstrate not just how an investigational drug performs, but how it compares to other available therapies and those under development. Such comparative effectiveness studies typically look at how the investigational therapy stacks up in terms of clinical effectiveness and safety profile, and also potential complications, tolerability, dosing strategies, and potential DDIs-all factors that could reduce long-term adherence to therapy and clinical outcomes for patients.

Modeled and predicted results help to extend available data produced in actual clinical trials, and answer key questions about how the drug performs in larger, virtual patient populations. This helps to produce the strongest case for drug developers to present to regulators and payers regarding how the drug is likely to perform in heterogeneous patients under real-world conditions.

The high cost and competitive landscape for oncology drugs have resulted in increased pressure from healthcare payers to justify adding the therapy to the formulary-another area for M&S, albeit related to health economics.

Today’s modeling toolbox

Exposure-response and pharmacokinetic/pharmacodynamic modeling incorporate PK and PD data gathered during early- and late-stage clinical trials. 

Exposure-response modeling is increasingly used to support optimal dose selection; provide proof-of-concept for the drug; elucidate and validate the treatment’s mechanism of action; improve characterization of relationships between drug exposure, efficacy, and toxicity; and inform interpretation of risk-benefit profiles. Such insight is essential not only for dose justification (which relies heavily on establishing dose-response relations) but for regulatory labels where dose modifications may be required for specific patient populations to avoid adverse events.

Population PK modeling is used to leverage sparse concentration data and evaluate the variability of drug exposure across individuals in a population over time. Such M&S approaches can facilitate not just development of new treatments for adults but also streamline pediatric drug development programs.

Physiologically-based PK (PBPK) modeling helps to predict the drug’s PK activity both in the body and the tumor site. PBPK models are built using preclinical and clinical trial data and can assess the therapy in simulated patient subpopulations to inform further clinical trial design and product labeling.

PBPK modeling is often use to predict DDIs but it can also extrapolate drug-function findings between patient cohorts, based on age and disease demographics and physiological differences (e.g., adult versus pediatric populations or cancer patients versus healthy volunteers). PBPK has gained significant traction with global regulators and PBPK-modeled results (in lieu of clinical studies) have been accepted by FDA to support more than 150 label claims.¹

Quantitative systems pharmacology (QSP) combines computational modeling and experimental methods to examine the mechanistic relationships between an investigational therapy, the biological system, and the disease process. QSP models can help to elucidate how target exposure, binding, and expression occur in biological pathways, impacting disease determinants, drug efficacy, and disease progression. This allows for optimal combinations and dosing regimens to be explored within a virtual population. QSP modeling is also used to reduce Phase II attrition by enabling a wide range of “what-if” scenarios to be investigated-ahead of the actual clinical investigation-to optimize trial design.

QSP models are also being used to simulate biomarker responses for a drug or multiple-drug regimen across virtual trials, providing added insight that can help to inform ongoing trial design. Meanwhile, many researchers consider QSP modeling an essential element for successful immuno-oncology drug discovery, because the number of possible combinations (in terms of drugs and dosing schedules) is simply too numerous to explore experimentally.²

Model-based meta-analysis (MBMA) is used to compare the investigational therapy with other drugs being tested in clinical trials or on the market. MBMA-demonstrated superiority for the drug candidate provides ongoing encouragement for investigators. In contrast, demonstration of non-superiority allows the drug developer to either leverage the findings and fine-tune the design of ongoing trials, or revise corporate priorities (perhaps focusing limited resources on more-promising candidates). MBMA can help to understand expected response in control arms (e.g., against the standard of care), and to identify a dose that is expected to be associated with competitive efficacy and safety outcomes in clinical trials.

MBMA is also used to provide “virtual comparator” data, to put into context benefits seen in single-arm trials.

Tumor growth modeling aims to better characterize tumor-size responses to therapy to establish the optimal dose regimen and therapeutic window and allow use of that relationship as an early marker for survival. Several promising oncology products have received their initial approval on the basis of tumor size in response to therapy (objective response data)-rather than survival outcomes. This places further emphasis on developing a thorough understanding of tumor-size dynamics and the effects of investigational drug candidates on tumor growth or shrinkage. As a result, novel approaches to tumor-size modeling are being developed and applied to support both drug development and regulatory decision-making.³

Closingthoughts

The strongest development program makes maximal use of all available data at the outset, and then applies M&S to answer essential questions, explore “what if?” scenarios, fill in critical gaps, and assess how the investigational therapy works and is tolerated in expanded patient subpopulations and different scenarios to those evaluated in the clinical trial dataset.

There is a growing body of evidence in academia, industry, regulatory agencies, and health authorities that M&S can facilitate development, approval, and cost justification of oncology drugs. This trend will continue.

References

1. PBPK Modeling and Simulation: Yesterday’s Scientific Endeavor is Today’s Regulatory Necessity. https://www.aapsnewsmagazine.org/articles/2017/dec17
2. “Model Firsts.” p 1-6.  BioCentury Innovations. August 2, 2018. 
3. The Model-informed Drug Development Imperative in Oncology R&D. https://www.certara.com/wp-content/uploads/Resources/Booklets/BK_MIDDImperativeOncology.pdf

Julie Bullock, PharmD, and Marc Pfister, MD, FCP, are vice presidents at Certara. Dr. Pfister is also Professor of Pharmacology and Pharmacometrics at the University of Basel.